On radiowave propagation paths in mobile communications, radiowaves coming from a transmission antenna undergo reflections and dispersions in accordance with surrounding topologies and the like, and reach a receiver in an aggregation of elementary waves. Since respective elementary waves differ in propagation path length and phase from one another, the fading phenomenon occurs due to the arrival of such an aggregate of elementary waves which have undergone reflections and dispersions. The fading phenomenon is always an impediment for accomplishing high quality mobile communications. The conquest of poor radiowave propagation environment due to this fading has been a challenge in the mobile communication technologies over a long time, and a variety of countermeasures have been so far brought into practice.
In recent years, moves have been activated to review the fading phenomenon as environmental resources which keep therein possibilities inherent in radiowave propagation in mobile communications, instead of treating the fading phenomenon as a bad fellow. Detailed descriptions thereon are disclosed in Gerard J. Foschini [1] and Emre Telatar [2].
Also, in recent years, there is also a move of making use of environmental resources inherent in radiowave propagation paths by utilizing spatial position independency in fading variations, called multi-USER Diversity, and this can be said to be one of trends similar to those mentioned above.
In a MIMO system, a transmission side spatially multiplexes and emits transmission series using a plurality of antennas which are not correlated to one another, while a reception side receives these signal series using a plurality of antennas which are not correlated to each other, and finds transmission series which would have been originally transmitted from the transmission side, based on the received signal series, in accordance with a most likelihood estimation. Such a MIMO system disproves the conventional idea about the fading phenomenon.
Each of the foregoing documents which have led the way in the MIMO system discloses spatial transmission processing called BLAST which efficiently makes use of spatially multiplexed signals as means for making use of propagation path resources inherent in a space which is transmission media in mobile communications. Also, as an architecture for implementing the spatial demultiplexing of BLAST with low complexity, an approach called V-BLAST is disclosed which is a combination of linear filtering with interference canceller. Linear filtering generally includes a zero-forcing (ZF) norm based one which performs the restraining (nulling) of interference components, or a minimum mean square error (MMSE) norm based one. As a linear transformation for performing the nulling in accordance with the ZF norm, a generalized inverse matrix of Moore-Penrose (MP) is known, wherein ordering processing is performed for detecting in an order in which a detected SNR (signal-to-noise ratio) is simply estimated to be the highest for purposes of improving the characteristics of the interference canceller. As an operation for ordering symbols, there is known to preferentially use a column vector which has a minimum norm corresponding to a weighting vector of the Moore-Penrose generalized inverse matrix.
Alternatively, a method based on QR resolution provides an approach which further reduces the complexity. Specifically, a communication path matrix (channel matrix) H is represented by H=Q·R through the QR resolution, the following relationship is established between an nT-th dimensional transmission antenna signal vector XεCnT×1 and a nR-th dimensional transmission antenna signal vector YεCnR×1:QH·Y=R·X+QH·v. 
It should be noted that matrixes and vectors are often written in bold letter according to convention, they are sometimes written in block letter for convenience of notation in this specification. Also, the transmission antenna signal vector is herein called the transmission signal vector, and the reception antenna signal vector is called the reception signal vector. Here, QεCnR×nR is a unitary matrix, and RεCnR×nT is an upper triangular matrix, where a noise component vector vεCnR×1 is unitarily transformed, so that the QR resolution does not result in noise emphasis, and the transformation carried out with maintaining the distance between signal points. In this QR based resolution process, step processing can be implemented, where vectors in a matrix can be reordered such that the processing can be performed in order from the highest SNR, and detection is made in such an order that SNR is maximized (ordering). Such a method is comparable to the nulling in the ZF norm, and essentially premises that the number nR of reception antennas is equal to or larger than the number nT of transmission antennas.
However, since these methods perform (nT−1)-th null production in nulling-based linear processing at the first step, they have a problem that the diversity gain can be provided only on the order of nR−nT+1. Therefore, detection errors are likely to occur at the first step, and its influence can cause error propagation which leads to detection errors at later stages.
On the other hand, an optimal detection is performed by performing a most likelihood detection (hereinafter abbreviated as MLD) in the following equation:
      X    MLD    =      arg    ⁢                  min                  X          ∈                                                  A                                                    n              T                                          ⁢                                                              Y              -                              H                ·                X                                                          2                .            
However, in MLD, since the complexity exponentially increases with respect to the number of antennas and the size |A| of a modulation signal point, MLD is effectively impossible in consideration of coding. Therefore, as an approach for reducing the complexity, an approach based on turbo principles and the like are under investigation. While the foregoing equation represents MLD only for a detector, the application of a decoding method called sphere decoding (hereinafter abbreviated as SD) has been proposed in order to avoid the complexity and avoid degraded characteristics due to error propagation from the first stage to later stages in the aforementioned V-BLAST, in other words, for purposes of producing a diversity gain in a fading environment. The basic idea of SD is such that a likelihood is calculated for signal points included in a sphere centered at a reception signal point and having proper radius r, and MLD is performed in a limited range. In SD, the efficiency depends on how radius r is selected. Alternatively, there is a method of avoiding the complexity by limiting the number of signal points with the magnitude of likelihood.
In this connection, Document [3] discloses estimation based on MMSE and the turbo principle, but does not touch the most likelihood estimation. In addition, the estimation is intended for channels, not for transmission series. Likewise, while Document [4] also discloses estimation based on MMSE and the turbo principle, the most likelihood estimation is not touched therein.
Also, as a technique for improving SNR under environment where radiowave propagation conditions are not satisfactory, there is a method using an array antenna from before [5]. However, the method using an array antenna premises that antennas which make up the antenna array are correlated to one another, and is essentially different from the MIMO based method which premises that there is no correlation among a plurality of antennas.
Now, the documents referred to in this Description are listed.
[1] Gerard J. Foschini, “Layered space-time architecture for wireless communications in a fading environment when using multiple antennas,” Bell Labs Technical Journal, Vol. 6, No. 2, pp. 41-59, Autumn 1996
[2] Emre Telatar, “Capacity of multi-antenna Gaussian channels,” European Transaction on Telecommunication, Vol. 10, No. 6, pp. 585-595, November/December 1999
[3] JP-2003-348057A
[4] JP-2003-152603A
[5] JP-2000-209018A